Applied Scientist

Grid•Seattle, WA

3d•Onsite

About The Position

Grid is seeking an Applied Scientist to join their team and contribute to building and scaling core product lines. This role involves working closely with product, engineering, and business leaders to leverage data for impactful outcomes. The position offers access to robust datasets and clear research objectives, enabling significant contributions to the company's progress and user success. Key projects include fraud detection, prevention, and mitigation in areas like risk underwriting for lending programs, and predictive analytics for payout and repayment systems.

Requirements

Bachelor's or Master's degree in Statistics, Mathematics, Physics, or Computer Science with a focus on machine learning.
Proven experience in Machine Learning and/or Applied Science.
Strong background in statistical inference and machine learning.
Deep expertise in applied machine learning, including statistical inference and predictive modeling.
Demonstrated practical experience with deep learning techniques, particularly transformer-based models.
Strong track record of reading, understanding, and implementing research papers in machine learning or related fields.
Hands-on experience with Python (with libraries like PyTorch/TensorFlow) and SQL.
Ability to work independently and take ownership of projects.
Proactive approach to identifying key leverage points for data products.
Proficiency in modern machine learning techniques, such as Model Evaluation and Validation, Deep Learning and Time Series Analysis, Logistic Regression, Naive Bayes, Tree based Models (i.e., Random Forest).
Confidence to prioritize work and deliver demonstrable results on a tight cadence.

Nice To Haves

Demonstrated experience or understanding of the financial industry, especially in building and scaling FinTech products.

Responsibilities

Perform data research and analysis using proprietary and external datasets.
Develop and validate models to achieve business objectives such as growth, fraud mitigation, and risk control.
Iterate on models based on team feedback and real-world performance.
Collaborate with data engineers and product managers to translate work into production-grade, scalable data products.
Present findings to team members with varying technical expertise.
Help build out the Applied Science and Machine Learning practice at Grid.